Dynamic-based representation inconsistency and implicit constraints for offline reinforcement learning.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning

Background:

• Offline reinforcement learning (RL) struggles with distributional shift and extrapolation errors from static datasets.
• Existing pessimistic methods for out-of-distribution (OOD) actions can overly restrict policy improvement.
• Balancing OOD exploration and exploitation is crucial for effective offline RL.

Purpose of the Study:

• To introduce Dynamic-based Representation Inconsistency and Implicit Policy Constraints Reinforcement Learning (DRIPC) for enhanced offline RL.
• To develop a novel uncertainty quantification mechanism for balancing exploration and exploitation.
• To improve policy performance and reduce computational overhead in offline RL.

Main Methods:

• Learning dynamic representations using ensemble models to quantify uncertainty via inconsistency.
• Applying pessimistic value iteration based on ensemble inconsistency.
• Reformulating policy constraints in Q-function space for reward-aware optimization and distribution alignment.
• Integrating ensemble models with implicit constraints.

Main Results:

• DRIPC achieves state-of-the-art performance on the D4RL benchmark.
• Demonstrates a 67.4% increase in average returns compared to Conservative Q Learning (CQL) on Antmaze tasks.
• Reduces computational overhead by 30.8% compared to previous ensemble-based methods while maintaining uncertainty characterization.

Conclusions:

• DRIPC effectively addresses challenges in offline reinforcement learning by intelligently balancing exploration and exploitation.
• The proposed uncertainty quantification and implicit constraint methods lead to significant performance gains.
• DRIPC offers a computationally efficient and robust solution for practical offline RL applications.